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Merge pull request #16858 from heavengate/fix_yolo_param

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Fix yolo param
revert-16839-cmakelist_change
Kaipeng Deng 6 years ago committed by GitHub
parent 102fc8596e 7b1702d9a1
commit 5d45eb06f9
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 25 additions and 25 deletions
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2
paddle/fluid/API.spec
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@ -358,7 +358,7 @@ paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes',
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1'))
paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e'))
paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gtscore', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '57fa96922e42db8f064c3fb77f2255e8'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '059021025283ad1ee6f4d32228cf3e4e'))
paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5566169a5ab993d177792c023c7fb340'))
paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0'))

36
python/paddle/fluid/layers/detection.py
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@ -509,14 +509,14 @@ def polygon_box_transform(input, name=None):
@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x,
gtbox,
gtlabel,
gt_box,
gt_label,
anchors,
anchor_mask,
class_num,
ignore_thresh,
downsample_ratio,
gtscore=None,
gt_score=None,
use_label_smooth=True,
name=None):
"""
@ -524,12 +524,12 @@ def yolov3_loss(x,
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
gt_box (Variable): groud truth boxes, should be in shape of [N, B, 4],
in the third dimenstion, x, y, w, h should be stored
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be in shape
gt_label (Variable): class id of ground truth boxes, shoud be in shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
@ -537,7 +537,7 @@ def yolov3_loss(x,
ignore_thresh (float): ${ignore_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss. Default None.
gtscore (Variable): mixup score of ground truth boxes, shoud be in shape
gt_score (Variable): mixup score of ground truth boxes, shoud be in shape
of [N, B]. Default None.
use_label_smooth (bool): ${use_label_smooth_comment}
@ -558,13 +558,13 @@ def yolov3_loss(x,
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
gtscore = fluid.layers.data(name='gtscore', shape=[6], dtype='float32')
gt_box = fluid.layers.data(name='gt_box', shape=[6, 4], dtype='float32')
gt_label = fluid.layers.data(name='gt_label', shape=[6], dtype='int32')
gt_score = fluid.layers.data(name='gt_score', shape=[6], dtype='float32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel,
gtscore=gtscore, anchors=anchors,
loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label,
gt_score=gt_score, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
@ -572,11 +572,11 @@ def yolov3_loss(x,
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gtbox, Variable):
if not isinstance(gt_box, Variable):
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gtlabel, Variable):
if not isinstance(gt_label, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if gtscore is not None and not isinstance(gtscore, Variable):
if gt_score is not None and not isinstance(gt_score, Variable):
raise TypeError("Input gtscore of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
@ -602,11 +602,11 @@ def yolov3_loss(x,
inputs = {
"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel,
"GTBox": gt_box,
"GTLabel": gt_label,
}
if gtscore:
inputs["GTScore"] = gtscore
if gt_score:
inputs["GTScore"] = gt_score
attrs = {
"anchors": anchors,

12
python/paddle/fluid/tests/test_detection.py
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@ -474,17 +474,17 @@ class TestYoloDetection(unittest.TestCase):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
gtscore = layers.data(name='gtscore', shape=[10], dtype='float32')
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
loss = layers.yolov3_loss(
x,
gtbox,
gtlabel, [10, 13, 30, 13], [0, 1],
gt_box,
gt_label, [10, 13, 30, 13], [0, 1],
10,
0.7,
32,
gtscore=gtscore,
gt_score=gt_score,
use_label_smooth=False)
self.assertIsNotNone(loss)

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